U.S. patent application number 14/407634 was filed with the patent office on 2015-08-20 for determining machine condition.
The applicant listed for this patent is Mikael Nilsson, Daniel Norder. Invention is credited to Mikael Nilsson, Daniel Norder.
Application Number | 20150234365 14/407634 |
Document ID | / |
Family ID | 49769084 |
Filed Date | 2015-08-20 |
United States Patent
Application |
20150234365 |
Kind Code |
A1 |
Nilsson; Mikael ; et
al. |
August 20, 2015 |
DETERMINING MACHINE CONDITION
Abstract
A set of load data for a selected point in time and resulting
from the machine operation is received. The load data is provided
from a first database comprising predefined machine conditions
associated to different sets of load data for the machine. One of
the predefined machine conditions that is most representative of
the received set of load data is selected.
Inventors: |
Nilsson; Mikael;
(Trollhattan, SE) ; Norder; Daniel; (Uddevalla,
SE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nilsson; Mikael
Norder; Daniel |
Trollhattan
Uddevalla |
|
SE
SE |
|
|
Family ID: |
49769084 |
Appl. No.: |
14/407634 |
Filed: |
June 19, 2012 |
PCT Filed: |
June 19, 2012 |
PCT NO: |
PCT/SE2012/000095 |
371 Date: |
May 7, 2015 |
Current U.S.
Class: |
700/275 |
Current CPC
Class: |
F01K 5/02 20130101; G05B
15/02 20130101; G06F 16/245 20190101; G05B 23/0283 20130101; G05B
13/024 20130101; G07C 5/008 20130101 |
International
Class: |
G05B 13/02 20060101
G05B013/02; G06F 17/30 20060101 G06F017/30; G05B 15/02 20060101
G05B015/02 |
Claims
1-24. (canceled)
25. A method, comprising: receiving a set of load data, for a
selected point in time, the load data including data from operation
of a machine; from a first database comprising predefined machine
conditions associated to different sets of load data for said
machine, selecting a predefined machine condition that is most
representative of said received set of load data.
26. The method of claim 25, wherein said set of load data comprises
values of a plurality of time dependent performance parameters
measured at a selected point in time during machine operation.
27. The method of claim 25, wherein each machine condition in said
first database comprise a unique identifier, the method further
comprising: providing an output comprising an identifier for the
machine condition most representative of said set of load data and
information identifying a machine session.
28. The method of claim 25, wherein said first database further
comprises a steady state condition corresponding to each machine
condition, each steady state condition being represented by a set
of load data performance parameter values.
29. The method of claim 28, further comprising: from a second
database comprising a set of pre-calculated machine condition
values for each of said predefined machine conditions in said first
database, retrieving a set of machine condition parameter values
corresponding to said machine condition identifier in said
output.
30. The method of claim 29, wherein selecting the predefined
machine conditions that is most representative of said received set
of load data comprises: selecting a machine condition from said
first database by matching a subset of said load data with
corresponding steady state condition values; defining a subset of
steady state conditions comprising said selected steady state
condition and a plurality of surrounding steady state conditions
based on a tolerance range of at least one parameter value of said
subset of load data; calculating the relative differences between
each parameter value of said subset of load data and corresponding
parameter values for each of said subset of steady state
conditions; adding said relative differences together for each
steady state condition; and selecting the steady state condition
having the smallest total difference.
31. The method of claim 25, wherein said selected point in time is
selected based on a predetermined selection criterion.
32. The method of claim 31, wherein said predetermined selection
criterion is a selection frequency for selecting a plurality of
sets of load data at a regular time-interval.
33. The method of claim 29, wherein said pre-calculated machine
condition values comprise machine condition values based on
previously measured load data and machine condition values based on
interpolated load data.
34. The method of claim 30, wherein, if more two or more steady
state conditions have a same relative difference, a steady state
condition corresponding to measured load data is selected over a
steady state condition corresponding to interpolated load data.
35. The method of claim 25, wherein the step of receiving a set of
load data comprises verifying that said load data are within a
predetermined range.
36. The method of claim 25, wherein said load data comprises
measured values of performance parameters influencing a mechanical
life length of components in said machine.
37. The method of claim 36, wherein said performance parameters
comprises at least one of vibration, stress, strain, engine
revolutions per minute, and ambient temperature.
38. The method of claim 25, wherein said machine condition
comprises at least one of engine pressure, temperature, mass flow,
and torque.
39. The method of claim 25, wherein said machine is an aircraft
engine, and said load data comprises aircraft altitude and aircraft
velocity.
40. The method of claim 25, further comprising predicting the life
consumption for said machine component based on the selected
determined machine condition.
41. The method of claim 40, wherein predicting life consumption of
a component further comprises: calculating at least one of
stresses, strains and temperature for a critical area of said
component based on said determined machine condition; and
predicting life consumption of said component for said load data
based on said at least one of the calculated stresses, strains and
temperatures.
42. A system, comprising: a first database comprising predefined
machine conditions associated to different sets of load data for a
machine; wherein said system includes a computer that is programmed
to: receive a set of load data, for a selected point in time,
resulting from said machine operation; and selecting a predefined
machine condition that is most representative of said received set
of load data.
43. The system of claim 42, further comprising a second database
comprising a set of pre-calculated machine condition values for
each of said predefined machine conditions in said first database,
the computer being further programmed to, from said second
database, retrieve a set of machine condition values corresponding
to a selected machine condition.
44. A computer readable medium having stored thereon instructions
for causing a processing unit to: receive a set of load data, for a
selected point in time, resulting from operation of a machine; and
from a first database comprising predefined machine conditions
associated to different sets of load data for said machine, select
a predefined machine conditions that is most representative of said
received set of load data.
45. The medium of claim 44, further comprising instructions to:
from a second database comprising a set of pre-calculated machine
condition values for each of said predefined machine conditions in
said first database, retrieve a set of machine condition values
corresponding to a selected machine condition.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a National Phase of, and claims priority
to, International Application No. PCT/SE2012/000095 filed on Jun.
19, 2012, of which application is hereby incorporated herein by
reference in its entirety.
BACKGROUND
[0002] Today, there is significant interest in improving the
prediction of the life consumption of individual components in a
machine, in particular machines with moving parts. By improving the
accuracy of such methods, the applied safety limits may be reduced,
and unnecessary replacement of components may be avoided. When
applied to an entire fleet (e.g., a military aircraft fleet) the
cost savings may be significant as well as allowing for an
increased operational lifetime. Furthermore, in the unusual event
that conventional methods are too optimistic, refined methods may
avoid failure of components, thus avoiding uncalculated stops in
operation or even more importantly accidents.
[0003] Examples of interesting applications where improved life
consumption predictions may be useful include aircrafts, gas/steam
turbines, trucks, loaders, nuclear plants and wind turbines.
[0004] A conventional method for predicting the life consumption of
a component in a machine is to measure one or a combination of the
usage/run time, distance or count the number of cycles of a
predefined load session or a conservative load session. A load
session is the time when the machine is in operation, for example
for an aircraft a load session may be defined as flying from point
A to point B with a predefined rotor speed variation.
[0005] In the field of aircrafts, the life consumption of an engine
is sometimes determined by making a "simplified" cycle count,
focusing on the usage of a specific engine component. There are
also available more specific and at least in some sense more
reliable methods where, e.g., ELCF (equivalent low cycle fatigue)
cycles for the specific, for example, engine component is
determined. Such ELCF cycles may for example be calculated based on
the high pressure rotor speed of an aircraft jet engine recorded
during a load session. The cycles may be determined by the number
of times the high pressure rotor speed exceeds certain selected and
predefined rotor speeds. Furthermore, to calculate the ELCF cycles,
scale factors are determined for the cycles based on predetermined
load sessions. However, a major drawback with ELCF cycles is that
the prediction of life consumption will have errors if the actual
load sessions experienced by a specific component differs
significantly from the predetermined load sessions, which the scale
factors are based upon.
[0006] As demands for cost efficiency and reliability increase, the
interest in finding better models for predicting life consumption
has also increased. This is made specifically apparent as the
conventional methods do not take all significant load cycles into
consideration. For example, the method of counting ELCF cycles only
considers one engine parameter of the entire engine while the life
consumption of the critical components an engine or machine may
vary depending which loads are most important for the life
consumption of the respective component.
[0007] In order to more accurately determine the life consumption
of, e.g., an engine, the life consumption for relevant components
in the engine must be determined. In order to determine the life
consumption of specific components, more detailed knowledge of
conditions in separate parts of the engine is required. As it is
difficult, or often impossible, to measure for example
temperatures, flows, and torques at relevant positions in the
engine, such parameters must be calculated based on measurements of
other parameters.
[0008] A drawback of such an approach is that calculations of such
parameters for different positions in the engine are both complex
and time consuming, thereby reducing the usability of a more
accurate method for determining life consumption of an engine.
SUMMARY
[0009] The present disclosure relates to a method for determining a
machine condition based on a received set of load data of a
machine, including determining machine condition parameters based
on measured performance parameters of a machine.
[0010] According to a first aspect of the present disclosure, a
method is provided for determining a machine condition indicative
of life consumption of a machine component subjected to loads
during machine operation, comprising the steps of: receiving a set
of load data, for a selected point in time, resulting from the
machine operation; from a first database comprising predefined
machine conditions associated to different sets of load data for
the machine, selecting one of the predefined machine conditions,
which is most representative of the received set of load data.
[0011] Load data should in the present context be understood as
data received from sensors in or relating to the active
machine.
[0012] A machine condition represents a set of parameters
influencing the life consumption of a machine component during
machine operation.
[0013] It should be noted that the database may refer to any data
structure suitable for storing data in an organized manner, such as
a file, a registry or the like.
[0014] The present disclosure is based on the realization that in
order to more efficiently determine machine condition parameters
which have to be calculated based on measured parameters, a
database comprising pre-calculated solutions for a large number of
sets of load data can be used. Thereby, instead of performing
calculations for each set of load data in an active session of the
machine, the resulting machine conditions may be found by matching
measured load data with corresponding predefined sets of load data
in the database, where each predefined set of load data correspond
to a machine condition. Such a matching procedure is generally more
time efficient than performing calculations for each individual set
of received load data. An additional advantage is that calculations
for load data resulting in non-converging solutions can be
avoided.
[0015] According to one embodiment, the set of load data may
comprise values of a plurality of time dependent performance
parameters measured at a selected point in time during machine
operation.
[0016] In one embodiment, each machine condition in the first
database may comprise a unique identifier, and the method may
further comprise the step of providing an output comprising an
identifier for the machine condition most representative of the set
of load data and information identifying a machine session.
[0017] Furthermore, said first database may further comprise a
steady state condition corresponding to each machine condition,
each steady state condition being represented by a set of load data
performance parameter values. Within the context of the
descriptiondisclosure, a steady state condition is here referred to
as a predetermined machine state, defined as a state of a machine,
at a specific point in time as defined by the specific values of
the load data.
[0018] According to one embodiment, the method may further comprise
the step of: from a second database comprising a set of
pre-calculated machine condition values for each of the predefined
machine conditions in the first database, retrieve a set of machine
condition parameter values corresponding to said machine condition
identifier in said output.
[0019] In some environments where measured performance parameters
need to be classified, it may be desirable to de-classify machine
operation load data so that machine conditions can be used for
further calculations without disclosing the measured performance
parameters. For example, when the machine is an aircraft engine and
the machine session is a flight mission, it may be desirable to
allow a third party to work with machine condition information for
calculating life consumption without revealing mission specific
parameters such as aircraft velocity and altitude.
[0020] This de-classification, or anonymization, of session data
may be achieved by using two separate databases where the first
database contains predetermined sets of measured values, steady
state conditions, corresponding to predetermined machine
conditions, and identifiers for the machine conditions, while the
second database contains the actual calculated parameter values of
the machine conditions identified by a steady state condition
identifier. Thereby, the first database can be classified and the
second database can be non-classified and thereby released for use
by external parties.
[0021] However, in applications where it is not required to isolate
performance parameters from resulting determined machine
conditions, the contents of the abovementioned first and second
databases may be provided in only one database.
[0022] According to one embodiment, selecting one of the predefined
machine conditions which is most representative of said received
set of load data may comprise: selecting a machine condition from
the first database by matching a subset of the load data with
corresponding steady state condition performance parameter values;
defining a subset of steady state conditions comprising the
selected steady state condition and a plurality of surrounding
steady state conditions based on a tolerance range of at least one
parameter value of said subset of load data; calculating the
relative differences between each parameter value of the subset of
load data and corresponding parameter values for each of said
subset of steady state conditions; adding the relative differences
together for each steady state condition; and selecting the steady
state condition having the smallest total difference.
[0023] As it is desirable to match each machine state to the steady
state condition most resembling the received set of load data, the
abovementioned procedure may advantageously be used to reach the
nearest steady state condition. However, alternative selection
procedures are also possible to use for reaching the steady state
condition closest to a specific set of load data.
[0024] In one embodiment, the selected point in time may be
selected based on a predetermined selection criterion, which may
advantageously be a selection frequency for selecting a plurality
of sets of load data at a regular time-interval. As continuously
measured parameters may be acquired with a relatively high
frequency, using a selection frequency for filtering the measured
data may be desirable to avoid having an excessive amount of data.
The selection frequency may for example be determined by the speed
of transient changes in the measured data. However, the selection
may also be performed based on the transient behavior of the
measured parameters such that more points are selected in periods
where fast transient behavior is observed compared to the
parameters are relatively constant over time. Alternatively, points
may be selected at predetermined arbitrarily defined instances or
intervals where it is desirable to determine a life consumption of
a component in a machine.
[0025] According to one embodiment, the pre-calculated machine
condition values may comprise machine condition values based on
previously measured load data and machine condition values based on
interpolated load data. The second database preferably comprises
machine condition parameters which are calculated from previously
measured or simulated load data. However, already for a relatively
modest number of measured parameters, the amount of possible load
data combinations quickly grows large. Therefore, it may not always
be possible to pre-calculate corresponding steady states and
resulting machine condition parameter values for all possible sets
of load data, as such measured data is not available. Instead, the
database may advantageously be padded with interpolated steady
states with resulting calculated machine condition values. Thereby,
a database having the desired resolution of steady state conditions
may be formed.
[0026] In one embodiment, if two or more steady state conditions
have the same relative difference, a steady state condition
corresponding to measured load data is selected over a steady state
condition corresponding to interpolated load data. As outlined
above, machine condition parameter values may result from
calculations or from interpolation. In the event that two steady
states are identified which have the same difference compared to a
given machine state, within a predetermined range, a steady state
condition corresponding to calculated machine condition values may
advantageously be selected as that may be seen as more
accurate.
[0027] According to one embodiment, the step of receiving a set of
load data may advantageously comprise verifying that the load data
is within a predetermined range. Measured data may be outside of a
tolerance range for reasons such as faulty sensors or due to other
errors. In such events, it may be desirable to abort the procedure
of determining a machine condition at an early stage which may be
achieved by comparing measured load data to predefined tolerance
ranges.
[0028] In one embodiment, load data may comprise measured values of
performance parameters influencing a mechanical life length of
components in said machine. By measuring performance parameters of
the machine influencing the mechanical wear and tear of components
in the machine, the resulting determined machine condition may be
used to determine the life consumption of specific components in
the machine.
[0029] Furthermore, the performance parameters may comprise
vibrations, stresses and/or strains measured at different locations
in the machine. Measurements of vibrations and stress/strain are
readily obtainable from conventional sensors such as accelerometers
and strain gauges. Moreover, measurements of vibration and
stress/strain may be made both on static structural elements as
well as on active elements such as components in an engine.
[0030] Additionally, the performance parameters may comprise engine
rpm (revolutions per minute) and/or ambient temperature. Parameters
such as engine rpm and ambient temperature, or temperatures at
selected positions in an engine, are readily obtainable through
conventional measurement methods.
[0031] In one embodiment, the machine condition values may
advantageously comprise engine pressure, temperature, mass flow
and/or torque. In contrast to the abovementioned measured
performance parameters, machine condition parameters such as engine
pressures at specific positions in an engine or mass flows are
often not possible to measure. Therefore, it is desirable to be
able to determine such parameters based on measurable
parameters.
[0032] According to one embodiment, the machine may be an aircraft
engine and the load data may comprise aircraft altitude and
aircraft velocity.
[0033] In a further embodiment, the load data may comprise recorded
loads from a flight mission of an aircraft.
[0034] According to a second aspect, it is provided a method for
predicting life consumption of a component in a machine, comprising
determining a machine condition according to any one of the
above-mentioned embodiments and predicting the life consumption for
the machine component based on the determined machine condition.
Predicting life consumption of a component may further comprise
calculating at least one of stresses, strains and temperature for a
critical area of the component based on the determined machine
condition; and predicting life consumption of the component for the
load data based on at least one of the calculated stresses, strains
and temperatures.
[0035] According to a third aspect, it is provided a system for
determining a machine condition indicative of life consumption of a
machine component subjected to loads during machine operation, the
system comprising: a first database comprising predefined machine
conditions associated to different sets of load data for the
machine; wherein the system is configured to: receive a set of load
data, for a selected point in time, resulting from the machine
operation; and selecting one of the predefined machine conditions,
which is most representative of the received set of load data.
[0036] The system may further comprise a second database comprising
a set of pre-calculated machine condition values for each of the
predefined machine conditions in the first database, the system
being configured to, from the second database, retrieve a set of
machine condition values corresponding to a selected machine
condition.
[0037] Effects and features of the second and third aspects of the
present invention are largely analogous to those described above in
connection with the first aspect.
[0038] According to a fourth aspect, it is provided a computer
program product comprising a computer readable medium having stored
thereon computer program means for causing a processing unit to
determine a machine condition indicative of life consumption of a
machine component subjected to loads during machine operation, the
computer program comprising: code for receiving a set of load data,
for a selected point in time, resulting from the machine operation;
code for, from a first database comprising predefined machine
conditions associated to different sets of load data for the
machine, selecting one of the predefined machine conditions, which
is most representative of the received set of load data.
[0039] The processing unit may preferably be provided in a server
or similarly, and the computer readable medium may be one of a
removable nonvolatile random access memory, a hard disk drive, a
floppy disk, a CD-ROM, a DVD-ROM, a USB memory, an SD memory card,
or a similar computer readable medium known in the art.
[0040] The computer program product may further comprise code for,
from a second database comprising a set of pre-calculated machine
condition values for each of the predefined machine conditions in
the first database, retrieving a set of machine condition values
corresponding to a selected machine condition.
[0041] Further effects and features of this fourth aspect of the
present invention are largely analogous to those described above in
connection with the first aspect.
[0042] Further features of, and advantages with, the present
disclosure will become apparent when studying the appended claims
and the following description. The skilled person realize that
different features of the present disclosure may be combined to
create embodiments other than those described in the following,
without departing from the scope of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0043] These and other aspects of the presently described subject
matter will now be described in more detail with reference to the
appended drawings showing an embodiment, wherein:
[0044] FIG. 1 schematically illustrates an overall maintenance
system for an aircraft;
[0045] FIG. 2 illustrates a cross-section of a jet engine
comprising a number of life limiting components/parts;
[0046] FIG. 3 is a flow-chart outlining the general steps of the
method according to an embodiment; and
[0047] FIG. 4 is a diagram schematically illustrating parts of the
method according to an embodiment.
DETAILED DESCRIPTION
[0048] The present subject matter will now be described more fully
hereinafter with reference to the accompanying drawings, in which
embodiments are shown. The present subject matter may, however, be
embodied in many different forms and should not be construed as
limited to the embodiments set forth herein; rather, these
embodiments are provided for thoroughness and completeness, and
fully convey the scope of the disclosed subject matter to the
skilled addressee. Like reference characters refer to like elements
throughout.
[0049] In the present detailed description, various embodiments of
a method for determining a machine condition according to the
present disclosure are mainly discussed with reference to machine
condition parameters in an aircraft engine. It should be noted that
this by no means limits the scope of the present disclosure which
is also applicable to other types of machines such as engines in
land based vehicles, boats and electrical machinery such as for
example wind power plants or water turbines.
[0050] FIG. 1 schematically illustrates an overall maintenance
system 100 for a machine. In FIG. 1 a fighter aircraft 102 is
illustrated as an example of the machine, the fighter aircraft 102
comprising a plurality of mechanical parts out of which some are
defined as critical life limited.
[0051] In FIG. 2 there is depicted a cross-section of a jet engine
200 comprising of a number of life limiting components 202, a jet
engine 200 being specifically exposed to forces that may cause
failure to its components/parts. Typically, several of the life
limiting components are rotating components and/or components
exposed to high temperatures or mechanical loads. A number of
parameters are measured in the jet engine during the time when the
machine is in operation (defined as an active machine session or a
load session), for example time, power level angle, altitude,
aircraft speed, ambient temperature, inlet temperature, low
pressure rotor speed, high pressure rotor speed, combustor
pressure, turbine outlet temperature, turbine outlet pressure,
control mode of, e.g., the aircraft 102. For the fighter aircraft
102 from FIG. 1, the plurality of measured parameter values is
recorded and stored in a computer storage medium (not shown)
available on the fighter aircraft 102.
[0052] With further reference to FIG. 1, the machine sessions with
recorded parameter values is transferred (e.g., wired or
wirelessly) to for example a data storage 104, possibly arranged on
the "ground", e.g., separate from the aircraft 102. The data
recorded during a flight is referred to as measured performance
parameter values from a machine session. The parameter values
stored in the data storage 104 are matched against parameter values
in a first database 106 in order to identify steady state
conditions. Identifiers of the steady state conditions are then
used to retrieve corresponding machine condition parameters from a
second database 108.
[0053] The machine condition parameters are used by a life
consumption calculation system 110 to predict the life consumption
of a component/part of e.g., the jet engine 200. The accumulated
life consumption results may be transferred to a maintenance unit
112. The maintenance unit 112 may, after an indication (e.g., a
determination made by the maintenance unit 112) that a component is
approaching the end of its useful life, determine a suitable
maintenance action. The maintenance action may for example be to
service the component or to replace it. When a maintenance action
has taken place, information of that (maintenance) event is sent
back to the life consumption calculation system 110, for example
comprising information as to that the component has been serviced
or exchanged for another new component, allowing the life
consumption calculation system 110 to adapt its calculations based
on the current life time state of the component. A (slightly) used
component may also be installed, whereas a predicted life
consumption adapted for the used component may be transferred from
the maintenance unit 112 to the life consumption system 110 in a
similar manner.
[0054] FIG. 3 is a flowchart outlining the general steps of a
method according to an embodiment.
[0055] In a first step 302, load data in the form of measured
performance parameters from a machine session are received. For an
aircraft, the session may be a flight mission and the measured
parameters may include velocity, altitude, ambient temperature,
turbine speed etc. In the present example, performance parameters
values are acquired at a frequency of about 10 Hz (ten Hertz). It
should however be noted that parameter values may be acquired at
any suitable frequency, in practice only limited by the capacity of
the data acquisition hardware.
[0056] Next, a filtering step 304 is performed where sets of the
acquired parameter values, i.e. sets of load data, are selected at
a frequency of 1/3 Hz which provides a sufficient time resolution
for a flight mission in the present application. Each set of
parameter values, i.e., the parameters for each selected point in
time here define a machine state.
[0057] Additional performance parameters may be calculated based on
the measured performance parameters either before or after the
filtering step. Furthermore, the measured parameter values may be
verified against predetermined tolerance ranges where values
falling outside a tolerance range either may trigger an alert or be
removed for further manual treatment of the corresponding machine
state.
[0058] In the next step 306, each of the machine states are matched
against steady state conditions stored in a first database. The
first database is created from previously measured performance
parameters defining steady state conditions in a multi-dimensional
grid, where each parameter represents a dimension in the grid. If
certain points in the database grid are missing, i.e., if the
measured performance parameters from which the database is formed
does not cover all points in the grid, such points in the grid may
be formed by interpolating between existing points. Furthermore, if
it is desirable to have a denser grid, for example, in regions
where it is known that parts of the machine is exposed to high
stress, a denser grid may be formed by interpolating between
existing points.
[0059] Typically, a subset of the measured parameter values for a
given machine state are used for matching to the grid in order to
improve the speed of the matching step. Furthermore, sufficient
accuracy may be achieved even if all measured parameters are not
matched. In the present example approximately ten parameters out of
thirty measured parameters are used for matching to the grid.
Accordingly, the dimensionality of the grid is equal to the number
of parameters to be matched.
[0060] For simplicity, the matching procedure will be described and
illustrated with reference to two measured performance parameters,
aircraft velocity (Mach number) and aircraft altitude. As
illustrated in FIG. 4, possible velocity and altitude combinations
may be drawn as a two-dimensional grid in a diagram 400 defining
the flight envelope 402 of the aircraft. In practice, the matching
is performed against a multi-dimensional grid comprising around 10
measured parameter values which may include, but are not limited
to, time, power level angle, altitude, aircraft speed, ambient
temperature, inlet temperature, low pressure rotor speed, high
pressure rotor speed, combustor pressure, turbine outlet
temperature, turbine outlet pressure and control mode.
[0061] The path 404 in the diagram corresponds to a mission and
each point on the path represents a machine state. Each
intersection of the grid corresponds to parameter values in the
first database for given a steady state condition.
[0062] First, an approximate matching is performed identifying a
number of steady state conditions near a machine state. Next, the
relative distance (difference) between the machine state (MS) and
the steady state (SS) points is determined for each of the
parameters (P) in the grid as
distance=abs(P.sub.Ms-P.sub.SS)/P.sub.MAX where P.sub.MAX is the
maximum allowed value of the parameter P. The relative differences
for all parameters matched to the grid are then added together for
each machine state, and the steady state condition having the
smallest total difference is selected.
[0063] An example illustrated in FIG. 4 shows MS.sub.1 having the
nearest steady state condition SS.sub.1. It may be so that two
steady state conditions are located at the same relative distance
from a machine state as illustrated by MS.sub.2, SS.sub.2 and
SS.sub.3. In such an event, a calculated steady state condition
resulting from measured parameters are selected before an
interpolated steady state condition.
[0064] Furthermore, if two steady state conditions located at the
same relative distance are both based on either measured or
interpolated values, the steady state condition may be selected
based on a proximity to the most recently matched machine state, or
based on possible steady state condition parameters for the most
recently matched machine state. Alternatively, the measured
parameters may be ranked with different priority so that the point
having the smallest difference to a highly prioritized parameter is
selected. However, with increasing dimensionality of the grid, the
likelihood of two points having the same relative difference is
rapidly decreasing.
[0065] After all machine states have been matched with their
nearest steady state conditions, an output is provided in step 308
which comprises identifiers of the steady state conditions. The
output may typically be comprised in a file comprising steady state
condition identifiers vs. time. The output may also comprise
metadata which identifies the mission and/or the engine and engine
components.
[0066] In the final step 310, machine condition parameters are
retrieved from a second database for each of the steady state
condition identifiers. The use of two databases enables the
anonymizing of measured parameters in environments such as for a
military aircraft where measured parameters such as Mach number and
altitude must be kept secret. However, for applications where
confidentiality is not an issue, one single database which
comprises both steady state conditions and the corresponding
machine condition values may be used. Machine condition parameters
comprise calculated pressures, mass flows, temperatures, torques,
etc. for different positions in the engine and relating to various
components of the engine. Thousands of parameters may be required
for accurately calculating a life consumption of individual
components in the engine. Based on the provided machine condition
parameters, thermal and mechanical loads in the form of stresses
strains and temperatures can be calculated as a step in determining
the life consumption of components resulting from a flight
mission.
[0067] Even though the presently disclosed subject matter has been
described with reference to specific exemplifying embodiments
thereof, many different alterations, modifications and the like
will become apparent for those skilled in the art. Variations to
the disclosed embodiments can be understood and effected by the
skilled addressee, from a study of the drawings, the disclosure,
and the appended claims. Furthermore, in the claims, the word
"comprising" does not exclude other elements or steps, and the
indefinite article "a" or "an" does not exclude a plurality.
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